Finite sample evidence on the performance of stochastic frontier models using panel data

1989 ◽  
Vol 1 (3) ◽  
pp. 229-261 ◽  
Author(s):  
Byeong-Ho Gong ◽  
Robin C. Sickles
Keyword(s):  
Panel Data ◽  
Stochastic Frontier ◽  
Finite Sample ◽  
Stochastic Frontier Models

On the use of panel data in stochastic frontier models with improper priors

1997 ◽  
Vol 79 (1) ◽  
pp. 169-193 ◽  
Author(s):  
Carmen Fernández ◽  
Jacek Osiewalski ◽  
Mark F.J. Steel
Keyword(s):  
Panel Data ◽  
Stochastic Frontier ◽  
Stochastic Frontier Models ◽  
Improper Priors

scholarly journals Specification Testing of Production in a Stochastic Frontier Model

2018 ◽  
Vol 10 (9) ◽  
pp. 3082 ◽  
Author(s):  
Xu Guo ◽  
Gao-Rong Li ◽  
Michael McAleer ◽  
Wing-Keung Wong
Keyword(s):  
Empirical Test ◽  
Stochastic Frontier ◽  
Real Data ◽  
Simulation Studies ◽  
Test Statistics ◽  
Local Smoothing ◽  
Finite Sample ◽  
Production Frontier ◽  
Specification Testing ◽  
Stochastic Frontier Models

Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for the plausibility of this application. In this paper, we develop procedures to test whether or not the parametric production frontier functions are suitable. Toward this aim, we developed two test statistics based on local smoothing and an empirical process, respectively. Residual-based wild bootstrap versions of these two test statistics are also suggested. The distributions of technical inefficiency and the noise term are not specified, which allows specification testing of the production frontier function even under heteroscedasticity. Simulation studies and a real data example are presented to examine the finite sample sizes and powers of the test statistics. The theory developed in this paper is useful for production managers in their decisions on production.

scholarly journals A NEW PANEL DATA TREATMENT FOR HETEROGENEITY IN TIME TRENDS

2012 ◽  
Vol 28 (3) ◽  
pp. 590-628 ◽  
Author(s):  
Alois Kneip ◽  
Robin C. Sickles ◽  
Wonho Song
Keyword(s):  
Panel Data ◽  
Time Trends ◽  
Stochastic Frontier ◽  
Estimation Method ◽  
Principal Component ◽  
Time Varying ◽  
Finite Sample ◽  
Cross Sectional ◽  
Semiparametric Method ◽  
Estimation Of Time

This paper introduces a new estimation method for arbitrary temporal heterogeneity in panel data models. The paper provides a semiparametric method for estimating general patterns of cross-sectional specific time trends. The methods proposed in the paper are related to principal component analysis and estimate the time-varying trend effects using a small number of common functions calculated from the data. An important application for the new estimator is in the estimation of time-varying technical efficiency considered in the stochastic frontier literature. Finite sample performance of the estimators is examined via Monte Carlo simulations. We apply our methods to the analysis of productivity trends in the U.S. banking industry.

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